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A fast consistent grid-based clustering algorithm. / Tarasenko, Anton S.; Berikov, Vladimir B.; Pestunov, Igor A. и др.

в: Pattern Analysis and Applications, Том 27, № 4, 139, 12.2024.

Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование

Harvard

Tarasenko, AS, Berikov, VB, Pestunov, IA, Rylov, SA & Ruzankin, PS 2024, 'A fast consistent grid-based clustering algorithm', Pattern Analysis and Applications, Том. 27, № 4, 139. https://doi.org/10.1007/s10044-024-01354-0

APA

Tarasenko, A. S., Berikov, V. B., Pestunov, I. A., Rylov, S. A., & Ruzankin, P. S. (2024). A fast consistent grid-based clustering algorithm. Pattern Analysis and Applications, 27(4), [139]. https://doi.org/10.1007/s10044-024-01354-0

Vancouver

Tarasenko AS, Berikov VB, Pestunov IA, Rylov SA, Ruzankin PS. A fast consistent grid-based clustering algorithm. Pattern Analysis and Applications. 2024 дек.;27(4):139. doi: 10.1007/s10044-024-01354-0

Author

Tarasenko, Anton S. ; Berikov, Vladimir B. ; Pestunov, Igor A. и др. / A fast consistent grid-based clustering algorithm. в: Pattern Analysis and Applications. 2024 ; Том 27, № 4.

BibTeX

@article{8887a07d51084ea38225cbc130350de7,
title = "A fast consistent grid-based clustering algorithm",
abstract = "We propose a fast consistent grid-based algorithm that estimates the number of clusters for observations in Rd and, besides, constructs an approximation for the clusters. Consistency is proved under certain conditions. The time complexity of the algorithm can be made linear retaining the consistency. Numerical experiments confirm high computational efficiency of the new algorithm and its ability to process large datasets",
keywords = "Big data, Clustering, Density level sets, Estimator for the number of clusters",
author = "Tarasenko, {Anton S.} and Berikov, {Vladimir B.} and Pestunov, {Igor A.} and Rylov, {Sergey A.} and Ruzankin, {Pavel S.}",
note = "The study of A.S. Tarasenko was supported by the Program for fundamental scientific research of the Siberian Branch of the Russian Academy of Sciences, project FWNF-2022-0010. The study of V.B. Berikov was supported by the Program for fundamental scientific research of the Siberian Branch of the Russian Academy of Sciences, project FWNF-2022-0015. The study of P.S. Ruzankin was supported by the Program for fundamental scientific research of the Siberian Branch of the Russian Academy of Sciences, project FWNF-2024-0001. The study of S.A. Rylov and I.A. Pestunov was supported within the state assignment of Ministry of Science and Higher Education of the Russian Federation for Federal Research Center for Information and Computational Technologies.",
year = "2024",
month = dec,
doi = "10.1007/s10044-024-01354-0",
language = "English",
volume = "27",
journal = "Pattern Analysis and Applications",
issn = "1433-7541",
publisher = "Springer London",
number = "4",

}

RIS

TY - JOUR

T1 - A fast consistent grid-based clustering algorithm

AU - Tarasenko, Anton S.

AU - Berikov, Vladimir B.

AU - Pestunov, Igor A.

AU - Rylov, Sergey A.

AU - Ruzankin, Pavel S.

N1 - The study of A.S. Tarasenko was supported by the Program for fundamental scientific research of the Siberian Branch of the Russian Academy of Sciences, project FWNF-2022-0010. The study of V.B. Berikov was supported by the Program for fundamental scientific research of the Siberian Branch of the Russian Academy of Sciences, project FWNF-2022-0015. The study of P.S. Ruzankin was supported by the Program for fundamental scientific research of the Siberian Branch of the Russian Academy of Sciences, project FWNF-2024-0001. The study of S.A. Rylov and I.A. Pestunov was supported within the state assignment of Ministry of Science and Higher Education of the Russian Federation for Federal Research Center for Information and Computational Technologies.

PY - 2024/12

Y1 - 2024/12

N2 - We propose a fast consistent grid-based algorithm that estimates the number of clusters for observations in Rd and, besides, constructs an approximation for the clusters. Consistency is proved under certain conditions. The time complexity of the algorithm can be made linear retaining the consistency. Numerical experiments confirm high computational efficiency of the new algorithm and its ability to process large datasets

AB - We propose a fast consistent grid-based algorithm that estimates the number of clusters for observations in Rd and, besides, constructs an approximation for the clusters. Consistency is proved under certain conditions. The time complexity of the algorithm can be made linear retaining the consistency. Numerical experiments confirm high computational efficiency of the new algorithm and its ability to process large datasets

KW - Big data

KW - Clustering

KW - Density level sets

KW - Estimator for the number of clusters

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85208745855&origin=inward&txGid=0ccc9264e339666245083e028d2553be

UR - https://www.mendeley.com/catalogue/04271cf9-673f-3cce-aeec-08a924e96184/

U2 - 10.1007/s10044-024-01354-0

DO - 10.1007/s10044-024-01354-0

M3 - Article

VL - 27

JO - Pattern Analysis and Applications

JF - Pattern Analysis and Applications

SN - 1433-7541

IS - 4

M1 - 139

ER -

ID: 61100322